{
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   "source": [
    "# The MIT License (MIT)\n",
    "\n",
    "# Copyright (c) 2020, NVIDIA CORPORATION.\n",
    "\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a copy of\n",
    "# this software and associated documentation files (the \"Software\"), to deal in\n",
    "# the Software without restriction, including without limitation the rights to\n",
    "# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of\n",
    "# the Software, and to permit persons to whom the Software is furnished to do so,\n",
    "# subject to the following conditions:\n",
    "\n",
    "# The above copyright notice and this permission notice shall be included in all\n",
    "# copies or substantial portions of the Software.\n",
    "\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS\n",
    "# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR\n",
    "# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER\n",
    "# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\n",
    "# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: Feature Engineering for Recommender Systems\n",
    "\n",
    "# 3. Feature Engineering - Categorical\n",
    "\n",
    "## 3.1. Combining Categories / Cross Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>*Combining Categories (CC)*</b> is a simple, powerful technique, but often undervalued. We will use this strategy in other feature engineering techniques, as well, and will introduce its value in a simple example.<br><br>\n",
    "In some datasets, categories by itself provide no information to predict the target. But if we combine multiple categories, together, then we can indentify patterns.<br><br>\n",
    "For example, we have the following categories:\n",
    "<li> Weekday</li>\n",
    "<li> Hour of the day</li>\n",
    "Each of them independently has no significant pattern in the dataset. If we combine them with *Weekday_HourOfTheDay*, then we can observe some strong behavior for certainn times on the weekend<br><br>\n",
    "Decision Trees determine the split in the dataset on single features. If each categorical feature by itself does not provide the information gain, then Decision Trees cannot find a good split. If we provide a combined categorical feature, the Decision Tree can easier split the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Combining categories*, also called Cross Column or Cross Product, is used in the [Wide Deep Architecture](https://arxiv.org/abs/1606.07792) by Google and is implemented in [Tensorflow](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython\n",
    "\n",
    "import cudf\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = [0]*45 + [1]*45 + [2]*10 + [0]*5 + [1]*5 + [2]*90 + [0]*5 + [1]*5 + [2]*90 + [0]*45 + [1]*45 + [2]*10\n",
    "f2 = [0]*45 + [0]*45 + [0]*10 + [1]*5 + [1]*5 + [1]*90 + [0]*5 + [0]*5 + [0]*90 + [1]*45 + [1]*45 + [1]*10\n",
    "t = [1]*45 + [1]*45 + [1]*10 + [1]*5 + [1]*5 + [1]*90 + [0]*5 + [0]*5 + [0]*90 + [0]*45 + [0]*45 + [0]*10\n",
    "\n",
    "data = cudf.DataFrame({\n",
    "    'f1': f1,\n",
    "    'f2': f2,\n",
    "})\n",
    "\n",
    "for i in range(3,5):\n",
    "    data['f' + str(i)] = np.random.choice(list(range(3)), data.shape[0])\n",
    "\n",
    "data['target'] = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   f1  f2  f3  f4  target\n",
       "0   0   0   0   0       1\n",
       "1   0   0   1   1       1\n",
       "2   0   0   0   2       1\n",
       "3   0   0   0   0       1\n",
       "4   0   0   1   2       1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take a look on the features *f1* and *f2*. Each of the feature provides no information gain as each category has a 0.5 probability for the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    mean  count\n",
       "f1             \n",
       "0    0.5    100\n",
       "1    0.5    100\n",
       "2    0.5    200"
      ]
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "data.groupby('f1').target.agg(['mean', 'count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    mean  count\n",
       "f2             \n",
       "0    0.5    200\n",
       "1    0.5    200"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   "source": [
    "data.groupby('f2').target.agg(['mean', 'count'])"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we analyze the features *f1* and *f2* together, we can observe a significant pattern in the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       mean  count\n",
       "f1 f2             \n",
       "0  0    0.9     50\n",
       "   1    0.1     50\n",
       "1  0    0.9     50\n",
       "   1    0.1     50\n",
       "2  0    0.1    100\n",
       "   1    0.9    100"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['f1', 'f2']).target.agg(['mean', 'count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we train a simple Decision Tree to show how combining categories will support the decision boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydotplus\n",
    "import sklearn.tree as tree\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hotn_features(df):\n",
    "    out = []\n",
    "    for col in df.columns:\n",
    "        if col != 'target':\n",
    "            out.append(pd.get_dummies(df[col], prefix=col))\n",
    "    return(pd.concat(out, axis=1))\n",
    "\n",
    "def viz_tree(df, lf):\n",
    "    dt_feature_names = list(get_hotn_features(df).columns)\n",
    "    dt_target_names = 'target'\n",
    "    tree.export_graphviz(lf, out_file='tree.dot', \n",
    "                         feature_names=dt_feature_names, class_names=dt_target_names,\n",
    "                         filled=True)  \n",
    "    graph = pydotplus.graph_from_dot_file('tree.dot')\n",
    "    return(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we train it without the combined categories *f1* and *f2*. We can see, that the Decision Trees creates the split on the random features *f3* and *f4*. The leaves have only a small information gain (e.g. 98 negative vs. 82 positive)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf = tree.DecisionTreeClassifier(max_depth=2)\n",
    "lf.fit(get_hotn_features(df), df[['target']])\n",
    "Image(viz_tree(df, lf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we combine the categories *f1* and *f2* as an additional feature. We can see that the Decision Tree uses that feature first and that the splits have a high information gain. For example, 190 negative vs. 110 positives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['f1_f2'] = df['f1'].astype(str) + df['f2'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf.fit(get_hotn_features(df), df[['target']])\n",
    "Image(viz_tree(df, lf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This simple technique will be used in combination with other feature engineering techniques.<br><br>\n",
    "We may have the idea - that is great, let's combine all categories into one feature. Unfortunately, this is not that easy. We want to balance the number of categories used, the number of observations in resulting category values and the information gain:\n",
    "<li> The more categories we combine, we will identify more underlying patterns - but combining more categories together reduces the number of observation per categoy in the resulting features\n",
    "<li> Higher number of observation in the resulting category shows a strong pattern and it is more generalizable\n",
    "<li> High information gain supports our model, but only if it is generalizable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The extreme example is that we combine all features f1, f2, f3 and f4 together. But the observation per category (count) is very small (4-20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>f3</th>\n",
       "      <th>f4</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"9\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.857143</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean  count\n",
       "f1 f2 f3 f4                 \n",
       "0  0  0  0   1.000000      6\n",
       "         1   1.000000      5\n",
       "         2   0.750000      4\n",
       "      1  0   0.750000      4\n",
       "         1   0.857143      7\n",
       "         2   1.000000      5\n",
       "      2  0   0.750000      4\n",
       "         1   0.750000      4\n",
       "         2   1.000000     11\n",
       "   1  0  0   0.000000      6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([x for x in df.columns if 'target' not in x and 'f1_f2' not in x]).target.agg(['mean', 'count']).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Best practicse:</b>\n",
    "<li> Combining low cardinal categories is a good start. For example, the dataset size is 100M rows and there are multiple categories with a caridnality (# of unique values) of 10-50, then combining them should not result in low observation count\n",
    "<li> Exploratory Data Analysis (EDA) is faster than training a model. Analyzing the information value for different combination of categorical features (on a sample) is really fast."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of getting the cardinality for categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>f3</th>\n",
       "      <th>f4</th>\n",
       "      <th>target</th>\n",
       "      <th>f1_f2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>136</td>\n",
       "      <td>142</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         f1   f2   f3   f4 target f1_f2\n",
       "count   400  400  400  400    400   400\n",
       "unique    3    2    3    3      2     6\n",
       "top       2    0    1    0      0    21\n",
       "freq    200  200  136  142    200   100"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.astype(str).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<li> Combining categories identifies underlying patterns in the dataset<br>\n",
    "<li> The technique can support Decision Trees to create better splits as Decision Trees analyze features independently of each other<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, it is your turn. What are good combinations of categories in our dataset?<br><br>\n",
    "\n",
    "**ToDo:**\n",
    "<li> Define which categorical featueres should be combined? Why should these be combined? What are your hypotheses?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cudf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = cudf.read_parquet('../data/train.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_time</th>\n",
       "      <th>event_type</th>\n",
       "      <th>product_id</th>\n",
       "      <th>brand</th>\n",
       "      <th>price</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_session</th>\n",
       "      <th>target</th>\n",
       "      <th>cat_0</th>\n",
       "      <th>cat_1</th>\n",
       "      <th>cat_2</th>\n",
       "      <th>cat_3</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>ts_hour</th>\n",
       "      <th>ts_minute</th>\n",
       "      <th>ts_weekday</th>\n",
       "      <th>ts_day</th>\n",
       "      <th>ts_month</th>\n",
       "      <th>ts_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-01 00:00:28 UTC</td>\n",
       "      <td>cart</td>\n",
       "      <td>17800342</td>\n",
       "      <td>zeta</td>\n",
       "      <td>66.90</td>\n",
       "      <td>550465671</td>\n",
       "      <td>22650a62-2d9c-4151-9f41-2674ec6d32d5</td>\n",
       "      <td>0</td>\n",
       "      <td>computers</td>\n",
       "      <td>desktop</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2019-12-01 00:00:28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-01 00:00:39 UTC</td>\n",
       "      <td>cart</td>\n",
       "      <td>3701309</td>\n",
       "      <td>polaris</td>\n",
       "      <td>89.32</td>\n",
       "      <td>543733099</td>\n",
       "      <td>a65116f4-ac53-4a41-ad68-6606788e674c</td>\n",
       "      <td>0</td>\n",
       "      <td>appliances</td>\n",
       "      <td>environment</td>\n",
       "      <td>vacuum</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2019-12-01 00:00:39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-01 00:00:40 UTC</td>\n",
       "      <td>cart</td>\n",
       "      <td>3701309</td>\n",
       "      <td>polaris</td>\n",
       "      <td>89.32</td>\n",
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       "      <td>a65116f4-ac53-4a41-ad68-6606788e674c</td>\n",
       "      <td>0</td>\n",
       "      <td>appliances</td>\n",
       "      <td>environment</td>\n",
       "      <td>vacuum</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2019-12-01 00:00:40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-01 00:00:41 UTC</td>\n",
       "      <td>cart</td>\n",
       "      <td>3701309</td>\n",
       "      <td>polaris</td>\n",
       "      <td>89.32</td>\n",
       "      <td>543733099</td>\n",
       "      <td>a65116f4-ac53-4a41-ad68-6606788e674c</td>\n",
       "      <td>0</td>\n",
       "      <td>appliances</td>\n",
       "      <td>environment</td>\n",
       "      <td>vacuum</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2019-12-01 00:00:41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-01 00:01:56 UTC</td>\n",
       "      <td>cart</td>\n",
       "      <td>1004767</td>\n",
       "      <td>samsung</td>\n",
       "      <td>235.60</td>\n",
       "      <td>579970209</td>\n",
       "      <td>c6946211-ce70-4228-95ce-fd7fccdde63c</td>\n",
       "      <td>0</td>\n",
       "      <td>construction</td>\n",
       "      <td>tools</td>\n",
       "      <td>light</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2019-12-01 00:01:56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                event_time event_type  product_id    brand   price    user_id  \\\n",
       "0  2019-12-01 00:00:28 UTC       cart    17800342     zeta   66.90  550465671   \n",
       "1  2019-12-01 00:00:39 UTC       cart     3701309  polaris   89.32  543733099   \n",
       "2  2019-12-01 00:00:40 UTC       cart     3701309  polaris   89.32  543733099   \n",
       "3  2019-12-01 00:00:41 UTC       cart     3701309  polaris   89.32  543733099   \n",
       "4  2019-12-01 00:01:56 UTC       cart     1004767  samsung  235.60  579970209   \n",
       "\n",
       "                           user_session  target         cat_0        cat_1  \\\n",
       "0  22650a62-2d9c-4151-9f41-2674ec6d32d5       0     computers      desktop   \n",
       "1  a65116f4-ac53-4a41-ad68-6606788e674c       0    appliances  environment   \n",
       "2  a65116f4-ac53-4a41-ad68-6606788e674c       0    appliances  environment   \n",
       "3  a65116f4-ac53-4a41-ad68-6606788e674c       0    appliances  environment   \n",
       "4  c6946211-ce70-4228-95ce-fd7fccdde63c       0  construction        tools   \n",
       "\n",
       "    cat_2 cat_3            timestamp  ts_hour  ts_minute  ts_weekday  ts_day  \\\n",
       "0    <NA>  <NA>  2019-12-01 00:00:28        0          0           6       1   \n",
       "1  vacuum  <NA>  2019-12-01 00:00:39        0          0           6       1   \n",
       "2  vacuum  <NA>  2019-12-01 00:00:40        0          0           6       1   \n",
       "3  vacuum  <NA>  2019-12-01 00:00:41        0          0           6       1   \n",
       "4   light  <NA>  2019-12-01 00:01:56        0          1           6       1   \n",
       "\n",
       "   ts_month  ts_year  \n",
       "0        12     2019  \n",
       "1        12     2019  \n",
       "2        12     2019  \n",
       "3        12     2019  \n",
       "4        12     2019  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         product_id    user_id      mean  count\n",
      "640663      1004767  545442548  0.000000    807\n",
      "620114      1004767  525325337  0.000000    753\n",
      "1560100     1005107  553431815  0.599185    736\n",
      "4882910    15300303  512875426  0.000000    709\n",
      "2021336     1005174  563599039  0.931238    509\n",
      "1148644     1004873  515032042  0.002041    490\n",
      "3626024     4804718  536911254  0.000000    471\n",
      "4154253     8800045  557590749  0.000000    380\n",
      "3076839     3601537  578263741  0.000000    363\n",
      "839819      1004833  564068124  0.793872    359\n",
      "1477094     1005100  611998200  0.000000    333\n",
      "6442        1002524  515598234  0.677215    316\n",
      "348093      1004249  513901034  0.648562    313\n",
      "1301035     1005008  521558076  0.003236    309\n",
      "1941657     1005161  512924342  0.537162    296\n",
      "1593381     1005115  516010934  0.750000    288\n",
      "3631591     4804718  576154686  0.550523    287\n",
      "5935044   100007950  515481166  0.000000    287\n",
      "3062423     3601489  513824664  0.000000    275\n",
      "85419       1002544  545376441  0.896296    270\n"
     ]
    }
   ],
   "source": [
    "###ToDo\n",
    "def explore_cat(df, cats):\n",
    "    df_agg = df_train[cats + ['target']].groupby(cats).agg(['mean', 'count']).reset_index()\n",
    "    df_agg.columns = cats + ['mean', 'count']\n",
    "    print(df_agg.sort_values('count', ascending=False).head(20))\n",
    "    \n",
    "cats = ['product_id', 'user_id']  \n",
    "explore_cat(df_train, cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "############### Solution ###############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ts_weekday  ts_hour      mean   count\n",
      "105           4        9  0.306273  135340\n",
      "104           4        8  0.308886  133648\n",
      "106           4       10  0.305982  130596\n",
      "103           4        7  0.302046  127421\n",
      "127           5        7  0.316684  126271\n",
      "102           4        6  0.285326  125225\n",
      "107           4       11  0.296363  125083\n",
      "128           5        8  0.364706  124991\n",
      "129           5        9  0.416071  123760\n",
      "152           6        8  0.414232  122685\n",
      "154           6       10  0.419844  122286\n",
      "151           6        7  0.413953  121625\n",
      "153           6        9  0.408690  121126\n",
      "130           5       10  0.405588  120901\n",
      "155           6       11  0.431274  120740\n",
      "126           5        6  0.315536  120658\n",
      "101           4        5  0.282200  117562\n",
      "131           5       11  0.351100  116699\n",
      "150           6        6  0.415361  115774\n",
      "156           6       12  0.426867  113457\n"
     ]
    }
   ],
   "source": [
    "cats = ['ts_weekday', 'ts_hour']  \n",
    "explore_cat(df_train, cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               cat_2    brand      mean    count\n",
      "3363           light  samsung  0.481047  1212393\n",
      "3165           light    apple  0.469441  1013391\n",
      "3405           light   xiaomi  0.396346   510657\n",
      "3258           light   huawei  0.460027   228978\n",
      "2298       headphone    apple  0.329330   121152\n",
      "3332           light     oppo  0.505053    98853\n",
      "3607        massager  samsung  0.406636    85472\n",
      "6757          vacuum  samsung  0.392459    68239\n",
      "6966          washer  samsung  0.334397    59208\n",
      "1293  coffee_grinder  lucente  0.511974    58294\n",
      "5231   refrigerators  samsung  0.449099    57455\n",
      "6656              tv  samsung  0.316777    53211\n",
      "6959          washer       lg  0.333735    47331\n",
      "2351       headphone   lenovo  0.455475    40483\n",
      "3431        massager    artel  0.391447    39354\n",
      "2293       headphone     acer  0.471679    38028\n",
      "3560        massager       lg  0.395045    34105\n",
      "5756         slipons  samsung  0.418726    30514\n",
      "5192   refrigerators  indesit  0.311972    28634\n",
      "6956          washer  indesit  0.344384    28596\n"
     ]
    }
   ],
   "source": [
    "cats = ['cat_2', 'brand']  \n",
    "explore_cat(df_train, cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypothesis:\n",
    "* Some user will always buy the same one-way products e.g. cleaning supplies, food\n",
    "* Behavior changes on weekday+hour - e.g. during the week, users will not stay up late as they work next day\n",
    "* Category and brand are both powerful features, but the combination can be more important. E.g. do people buy apple smartphones or accessories?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "############### Solution End ###########"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is not much optimization technique to apply. We will \"chain\" the idea of combining categories with other Feature Engineering techniques, which does NOT require us to actually combine and store the new feature in the dataset. Instead, we will create features based on the combined categories directly and won't store the combined categories as a separate feature.<br><br>\n",
    "One advice is to use cuDF instead of pandas. Analzying the dataset requires calculating different groupby combination multiple times by a data scientist. GPU acceleration can significantly speed-up the calculations and enables you to run more comparisons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_df = df_train.to_pandas()\n",
    "big_data = df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas Shape:(11461357, 19)\n",
      "cudf Shape:(11461357, 19)\n"
     ]
    }
   ],
   "source": [
    "print('Pandas Shape:' + str(big_df.shape))\n",
    "print('cudf Shape:' + str(big_df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 3.02 s, sys: 616 ms, total: 3.63 s\n",
      "Wall time: 3.63 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "big_df.groupby(['cat_0', 'cat_1', 'cat_2', 'cat_3', 'brand']).target.agg(['mean', 'count'])\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 812 ms, sys: 800 ms, total: 1.61 s\n",
      "Wall time: 1.61 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "big_data.groupby(['cat_0', 'cat_1', 'cat_2', 'cat_3', 'brand']).target.agg(['mean', 'count'])\n",
    "print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dataset with 12M rows is ~4-6x faster on GPU with cuDF as on CPU with pandas. This difference can even increase with larger dataset size as the groupby operation is not linear in complexity. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shutdown the kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': 'ok', 'restart': False}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app = IPython.Application.instance()\n",
    "app.kernel.do_shutdown(False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
